8
Radiosonde Observations

Summary of Trends

Several data sets compiled from radiosonde observations have
been used to monitor atmospheric temperature trends (Angell and
Korshover, 1975; Angell, 1988; Parker et al., 1997). Recent
analyses of various versions of these data sets indicate slight
warming trends of up to 0.1 °C/decade or more in lower
tropospheric temperature for the period during which MSUs have been
operational (1979–98). Exact trend values vary depending on
the data source, treatment, and trend-fitting method (e.g., Angell,
1999; Parker et al., 1997; Santer et al., 1999; Santer et al.,
2000).

Sources of Uncertainty in Trend
Estimates

There are several unresolved challenges in determining reliable,
global, radiosonde-based temperature trend estimates. These
challenges are outlined below, together with some of the
methodologies that have been used to address them.break

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8
Radiosonde Observations
Summary of Trends
Several data sets compiled from radiosonde observations have
been used to monitor atmospheric temperature trends (Angell and
Korshover, 1975; Angell, 1988; Parker et al., 1997). Recent
analyses of various versions of these data sets indicate slight
warming trends of up to 0.1 °C/decade or more in lower
tropospheric temperature for the period during which MSUs have been
operational (1979–98). Exact trend values vary depending on
the data source, treatment, and trend-fitting method (e.g., Angell,
1999; Parker et al., 1997; Santer et al., 1999; Santer et al.,
2000).
Sources of Uncertainty in Trend
Estimates
There are several unresolved challenges in determining reliable,
global, radiosonde-based temperature trend estimates. These
challenges are outlined below, together with some of the
methodologies that have been used to address them.break

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Background
Radiosondes have for several decades been the primary means of
obtaining atmospheric vertical profile data from the surface to the
lower stratosphere. They are routinely used as input to the
operational meteorological analyses that are used in numerical
weather prediction and meteorological diagnostics. In the absence
of other in situ measurements, radiosonde observations have
recently been used to assess trends of atmospheric conditions above
the surface, even though they were not designed for this purpose.
The instrument packages carried aloft by balloons are generally
equipped with temperature, humidity, and pressure sensors, whose
measurements are radio-transmitted to a ground receiving station.
Wind data are also obtained by tracking the position of the
instrument during ascent. Temperature sensors vary according to the
manufacturer and model of the radiosonde; most contemporary
instruments carry a thermocapacitor, wire resistor, thermocouple,
or bimetallic sensor. An excellent overview of radiosonde
instruments, including discussion of measurement error
characteristics, is provided by the World Meteorological
Organization (WMO, 1996).
Currently, the global radiosonde network nominally includes
about 900 upper-air stations, of which about two-thirds make
observations twice daily (at 0000 and 1200 Coordinated Universal
Time (UTC)). The network is predominantly land-based and favors the
Northern Hemisphere (Figure 2.6). Radiosondes can achieve heights
of about 35 km, although many soundings terminate below 20 km
because less expensive balloons burst at a lower altitude. There
has been some deterioration in the radiosonde network in recent
years. The loss of navigational systems used to track the sondes
has led to at least temporary closing of some stations,
particularly in Africa. Efforts to reduce operating costs have led
to station closures and reduced observing schedules in some parts
of the former Soviet Union and elsewhere.
Measurements are made and transmitted with approximately
10–50 m resolution during ascent, but archived sounding data
may contain only about 20 data levels per sounding.
Radiosonde-based data sets for climate monitoring come from two
basic data products: individual soundings containing all reported
data (Angell, 1988), or monthly mean data (known as CLIMAT TEMP
reports) at mandatory pressure levels only (Parker et al., 1997).
Only about 45% of stations provide CLIMAT TEMP reports in addition
to the daily sounding data. Missing daily datacontinue

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can cause substantial random errors in estimates of monthly
means, especially if the available data are unevenly distributed in
time.
Data Homogeneity Problems
Sampling Changes: The spatial and temporal
characteristics of radiosonde observations have tended to change
through time, in part because these observations are generally made
for operational weather analysis purposes rather than long-term
trend detection. Spatial biases may be introduced through changes
in the number, location, and characteristics of radiosonde sites.
Further exacerbating this problem is the fact that the land surface
characteristics of radiosonde sites may change through time,
biasing surface and boundary layer observations. Shifts in site
locations of even a few kilometers can have a similar effect. In
addition to spatial sampling issues, biases can also be introduced
if the diurnal sampling time or frequency changes. For example,
observing times were not fixed at 0000 and 1200 UTC until 1957,
making data from earlier years potentially biased relative to more
recent observations. Even today, measurements are not always
conducted twice daily at all stations.
Instrument Changes: There have been many and widespread
changes of radiosonde sensors during the history of the global
radiosonde network. These changes often brought useful improvements
in precision and accuracy, essential for weather analysis and
forecasting, but they also prejudiced the homogeneity of the
records from the perspective of climate change analysis (Gaffen,
1994). For example, efforts to update the sonde's
temperature-sensing technology and efforts to mitigate the effects
of solar radiation on the sondes have, in some cases, introduced
time varying biases. Parker and Cox (1995) documented an increased
dominance of radiosondes made by the Vaisala company in recent
years. Since their paper was published, many North American
stations have also switched to Vaisala instruments, although
stations in Russia, China, and Japan continue to use national
instrumentation. Within each class of radiosondes (such as
Vaisala), there have also been progressive developments (e.g., the
Vaisala RS11 through RS90 series), which have introduced
heterogeneity. Another problem is that the documentation of
instrument types and the timing of instrument changes is not always
complete or accurate (Gaffen, 1993).break

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Data Treatment Changes: Changes in the way raw radiosonde
observations are processed can also have a significant impact on
the long-term record. Changes in corrections applied to the
temperature data (to reduce errors resulting from solar and
infrared radiation impinging on the sensor and errors resulting
from the time lags in instrument response as the sensor ascends
through the atmosphere) are detectable in the data (Gaffen, 1994).
However, these effects are more noticeable in the data from the
upper troposphere and lower stratosphere than from the lower
troposphere, and more noticeable early in the radiosonde record
than in recent years (the time of overlap with MSU observations). A
third type of change in data treatment affects the CLIMAT TEMP
monthly averages, but not the individual sounding data. Changes in
the rules by which stations compute their monthly averages,
including which observing time to use and how many days of data
must be available, can have large effects, which are revealed by
comparisons with monthly averages computed using a consistent set
of rules (Gaffen et al., 2000).
Variety of Methods of Estimating
Global Trends in Layer-Mean Temperatures
Methods for Obtaining Layer-Mean Temperatures: The MSU
temperature product discussed in the previous chapter is a
vertically-broad and non-uniform representation of tropospheric
temperature. Therefore, comparisons with radiosonde data are most
meaningful if the radiosonde data are processed to represent the
same portion of the atmosphere as the MSU product. At least two
different techniques have been used. The simplest involves
computing the mean mid-tropospheric temperature, weighting all
levels (e.g., 850 to 300 hPa) equally. The disadvantage of this
method is that it does not reflect the unequal contributions from
each of the levels that underlie the MSU product. In the second
technique, radiosonde temperature data at different altitudes are
weighted to more closely resemble what the satellite would have
observed. However, this method, termed the 'static weighting
method,' does not account for variations in atmospheric moisture as
a function of space or time. The biases associated with this
technique are, however, not large relative to other sources of bias
in the global time series (Santer et al., 1999).break

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Methods for Calculating Global Average Temperature
Anomalies: Estimates of global and regional temperature
anomalies depend on the selection of stations, the method of
averaging station anomalies, the method of gridding, and the method
of averaging gridded values, which are discussed below.
For the reasons mentioned previously in the Sampling
Changes section, fixed networks smaller than the full observing
system have been chosen for the determination of temperature
trends. Angell and Korshover (1975) selected 63 stations in their
pioneering efforts to develop a global temperature monitoring
capability. The Global Climate Observing System / Baseline
Upper-Air Network designates approximately 150 stations for the
same purpose. Other efforts (e.g., Oort and Liu, 1993, Parker et
al., 1997) attempt to incorporate data from as many stations as
possible with an aim of maximizing spatial and temporal coverage.
That approach, however, suffers from the inconsistencies that are
introduced into the network through time.
An annual temperature anomaly of the selected stations may be
calculated as the average of the available months, or as an average
of the available seasons. If the record is incomplete in a
systematic manner, the weighting implicitly applied to individual
monthly data may introduce biases. Similar considerations apply to
the calculation of monthly statistics from daily data, and these
are especially relevant when a month's data consist of a different
number of daytime and night-time ascents.
To grid the station data, anomalies within a gridbox may either
be weighted equally, or weighted according to the distance of the
station from the center of the gridbox (e.g., Parker et al., 1997).
Some schemes also fill unsampled gridboxes using eigenvector-based
reconstructions (Parker et al., 1997), objective analysis schemes
such as ''Conditional Relaxation" (Oort and Liu, 1993), or optimum
interpolation (used on sea surface temperatures by Reynolds and
Smith, 1994). The validity of interpolations decreases away from
the observation sites in proportion to the spatial decorrelation
scale of the temperature data. Finally, model-based reanalyses,
which also incorporate surface and satellite observations, use
complex data-assimilation schemes based upon known physical
relationships (e.g., geostrophy) and detailed statistical quality
controls (Kalnay et al., 1996; Uppala, 1997).
There are many ways of averaging gridded temperature anomaly
values into global indicators. Although each of these has its
strengths,continue

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the problem of incomplete spatial coverage remains nonetheless.
The most direct method is to average all of the gridded anomalies,
weighting each according to the area of the gridbox (Parker et al.,
1997). This approach naturally places more weight on the Northern
Hemisphere where there are more observations. Alternatively, the
grid boxes may first be averaged into larger grid boxes before
averaging globally (e.g., Parker et al., 1997). In data-sparse
regions, this procedure gives greater weight to isolated boxes
where data happen to exist, and was shown by Santer et al. (1999)
to have a noticeable impact on trend estimates. Another method is
to calculate latitudinal averages and then to average these bands
(e.g., Angell, 1988). For the tropics, where data are sparse but
also where temperature variations tend to be spatially coherent,
this form of averaging offers some potential advantages (Wallis,
1998). On the other hand, it can introduce large errors if
relatively few stations are operating within a given latitudinal
band, and if their temperatures are not representative of the
latitudinal average.
Efforts to Correct the Problems
It is only in the past few years that serious attempts have been
made to adjust radiosonde data to remove the effects of artificial
changes, and several different approaches have been proposed.
Because of the demonstrated sensitivity of trends to data
adjustments, and the distinct possibility that some adjustments may
introduce more error than they remove, it will be important to
compare adjusted data sets and their effects on trends in the
future.
Parker et al. (1997) used MSU retrievals as references to test
for heterogeneities in temperatures at individual radiosonde
stations since 1979 (the beginning of the MSU record), and to make
adjustments if necessary. MSU channel 4 and the 2LT retrieval were
used for the stratosphere and troposphere, respectively.
Adjustments were only made in cases of known instrumental or
procedural changes at the radiosonde stations. Some changes in
instrumentation had resulted in spurious cooling of up to 3 °C
in the lower stratosphere, but biases were much smaller in the
troposphere.
Radiosonde temperatures can be adjusted independently of MSU
data using semi-empirical models of the thermodynamics of
radiosondes, and the results verified using day-night (strictly,
0000–1200 UTC)break

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differences (Luers and Eskridge, 1998). These models take into
account known changes in the rate of ascent and in observing time,
as well as changes in sensors. These models are now being applied
to the majority of radiosonde types used since 1960.
Figure 8.1.
Mid-tropospheric (500 hPA) temperature trends (and their 95% confidence intervals)
at twelve radiosonde stations operated by the Australian Bureau of Meteorology for
1959–1995 (Gaffen et al., 2000; reprinted with permission of the American Meteorological
Society). The temperature data for seven stations were adjusted to account for a 1979 change
in radiosonde instrument type from the Astor Mark I sonde to the Phillips Mark II sonde.
A third approach utilizes statistical methods to objectively
identify abrupt shifts, or change-points, in time series. Gaffen et
al. (2000) experimented with two different statistical approaches,
representing opposite extremes. One, which relies only on
statistical identificationcontinue

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and adjustment of the data, is very liberal in that it cannot
distinguish between artificial and natural variability. The other,
which incorporates station history metadata, is very conservative
in that it adjusts only for artificial changes which are identified
with a high degree of confidence. These experiments demonstrate
that adjustments for change-points can yield very different time
series and trends, depending on the scheme used to make adjustment
and the manner in which it is implemented. This is illustrated in
Figure 8.1, which shows mid-tropospheric (500 hPa) temperature
trends from twelve stations operated by the Australian Bureau of
Meteorology (Gaffen et al., 2000). The trends in the original data
for the period 195995 show warming of between 0.05 and 0.71
°C/decade. The data from seven stations were adjusted due to a
step-like warming of approximately 0.75 °C associated with a
1979 change in radiosonde type. The effect of the adjustment is to
substantially reduce the trends and in some cases to change the
warming to a cooling.
Model-based reanalyses (see the previous discussion on gridding
radiosonde data) offer a further potential means of radiosonde
temperature bias detection and removal through comparisons with
first-guess fields.
Each of these strategies for radiosonde data adjustment, except
the last one, depends to some degree on metadatainformation
about the history of instruments and observing practices at each
station. Despite recent efforts to compile and digitize global
radiosonde metadata (Gaffen, 1993, 1996), there are gaps and
uncertainties in the historical information. Current efforts to
collect and maintain metadata archives are minimal and should be
enhanced.break